Payment Reconciliation ModuleEngineered a file upload system using AWS S3 and RabbitMQ for asynchronous background processing, scaling upload capacity by 10x and reducing delays reported by stakeholders.Designed a high-performance Redis-cached microservice to support 300K+ daily transactions, leveraging LRU caching to minimize processing times by 40%.Secured data synchronization for five internal systems by implementing RESTful APIs with OAuth 2.0, reducing sync failures from 50… Show more Payment Reconciliation ModuleEngineered a file upload system using AWS S3 and RabbitMQ for asynchronous background processing, scaling upload capacity by 10x and reducing delays reported by stakeholders.Designed a high-performance Redis-cached microservice to support 300K+ daily transactions, leveraging LRU caching to minimize processing times by 40%.Secured data synchronization for five internal systems by implementing RESTful APIs with OAuth 2.0, reducing sync failures from 50 to 5 per day.Invoice Data Extraction ModuleDeveloped a fault-tolerant batch processing pipeline using Kafka, increasing throughput from 5K to 6.5K invoices/hour by optimizing data aggregation and parsing techniques.Built a PyTest-based testing framework for validating extracted invoice data, improving product reliability and reducing error rates during processing.Streamlined database interactions by eliminating N+1 SQL calls, reducing query execution time and enhancing the overall responsiveness of the system. Show less

Automated SAP sales data pipelines using Azure Logic Apps and Python Functions, improving report consistency by 50% and minimizing manual interventions.Implemented monitoring and logging systems with Azure Monitor and Log Analytics, reducing incident response times by 20% and improving system reliability.Collaborated with cross-functional teams to deliver solutions that enhanced data accuracy and operational efficiency, driving business success.

• Participated in requirements elicitation and Utilized unsupervised clustering algorithms for customer segmentation.• Applied feature engineering techniques to enhance the effectiveness of clustering algorithms.• Utilized Python for predictive modeling and executed SQL queries for data analysis, enabling proactive targeting.